:train_guassian_model
python train.py -s data\tandt\train -m output\train

:train_embedding_model
REM loss not decrease (lr is too small to drive)
python train_extractor_tv.py -s data\tandt\train -m output\train --key appearance --data_device cuda -P SiLUMLP -lr 2e-6 2e-4
REM converge to loss ~0.01 (possibly overfitted)
python train_extractor_tv.py -s data\tandt\train -m output\train --key appearance --data_device cuda -P SiLUMLP -lr 2e-5 1e-3
REM converge to loss ~1.0? (possibly a good setting)
python train_extractor_tv.py -s data\tandt\train -m output\train --key appearance --data_device cuda -P SiLUMLP -lr 2e-6 1e-3

REM try cossine dist?
python train_extractor_tv.py -s data\tandt\train -m output\train --key appearance --data_device cuda -P SiLUMLP --loss cos -lr 2e-6 1e-3
python train_extractor_tv.py -s data\tandt\train -m output\train --key appearance --data_device cuda -P SiLUMLP --loss abscos -lr 2e-6 1e-3
